Gender: Male 
Age: 18-24
Nationality: United States
Ethnicity: Black or African American 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Undergraduate degree
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 8
Home: Rent 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Listen to hiphop and rap;Exercise;Use social media;Watch sports;Attend football matches 
Devices used daily: Laptop;Smartphone;Tablet 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Grammar checker (i.e., correcting grammar mistakes);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
Frequency of LLM use: Every day
LLMs used: ChatGPT;Claude;GitHub Copilot;Google Gemini;Jasper;Microsoft Bing AI;Perplexity;Character.AI 
LLM use cases: Coding;Translation;Brainstorming;Writing (e.g., composing emails);Proofreading/Editing (e.g., grammar, spelling, structure improvements);Digital content creation (e.g., creation of social media content);Generic chatbot conversation;Analyze data;Generate art;Learning (e.g., explaining complex concepts in simple terms);Get information about current events;Answering questions about general knowledge;Summarizing long texts 
Contexts of LLM use: Work;School/University;Learning;Creative or artistic;Technical;Personal 
Prompt 1: Effective Interview Techniques
Prompt 2: Aptitude Test Overview
Prompt 3: Mistaken Shootings in USA
Prompt 4: Cybersecurity and Hosting Budget
Prompt 5: Arizona Injunctions: Proper Service
Prompt 6: Proper Service in Law
Prompt 7: Solving Equations with 2 Variables
Prompt 8: Organizational Chart Creation
Prompt 9: Home Discussion: Summarize Please
Prompt 10: Emergency Codes in Healthcare
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